L. Heinrich
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View article: Automatic Modeling and Object Identification in Radio Astronomy
Automatic Modeling and Object Identification in Radio Astronomy Open
View article: Machine learning for the cluster reconstruction in the CALIFA calorimeter at R3B
Machine learning for the cluster reconstruction in the CALIFA calorimeter at R3B Open
View article: Developments in Ground-Based Space Weather Monitoring: GIFDS and CALLISTO for Event Analysis
Developments in Ground-Based Space Weather Monitoring: GIFDS and CALLISTO for Event Analysis Open
The International Space Weather Initiative (ISWI) features several instruments, two of which this paper is focusing on: GIFDS and CALLISTO. The original receivers have been further developed aiming a small network of 4 combined stations. M…
View article: Large physics models: towards a collaborative approach with large language models and foundation models
Large physics models: towards a collaborative approach with large language models and foundation models Open
This paper explores the development and evaluation of physics-specific large-scale AI models, which we refer to as large physics models (LPMs). These models, based on foundation models such as large language models (LLMs) are tailored to a…
View article: Double Descent and Overparameterization in Particle Physics Data
Double Descent and Overparameterization in Particle Physics Data Open
Recently, the benefit of heavily overparameterized models has been observed in machine learning tasks: models with enough capacity to easily cross the \emph{interpolation threshold} improve in generalization error compared to the classical…
View article: Evaluation of “Real BVM Help” for Improving Manual Ventilation Quality in the Prehospital Setting: A Before-After Manikin Study
Evaluation of “Real BVM Help” for Improving Manual Ventilation Quality in the Prehospital Setting: A Before-After Manikin Study Open
Our data demonstrate significant improvements in ventilation rates and volumes when using a ventilation feedback device. This manikin study suggests a ventilation feedback device being beneficial for the use by EMS members, but our finding…
View article: HGPflow: extending hypergraph particle flow to collider event reconstruction
HGPflow: extending hypergraph particle flow to collider event reconstruction Open
In high energy physics, the ability to reconstruct particles based on their detector signatures is essential for downstream data analyses. A particle reconstruction algorithm based on learning hypergraphs (HGPflow) has previously been expl…
View article: Analysis Facilities for the HL-LHC White Paper
Analysis Facilities for the HL-LHC White Paper Open
This white paper presents the current status of the R&D for Analysis Facilities (AFs) and attempts to summarize the views on the future direction of these facilities. These views have been collected through the High Energy Physics (HEP) So…
View article: Machine Learning for the Cluster Reconstruction in the CALIFA Calorimeter at R3B
Machine Learning for the Cluster Reconstruction in the CALIFA Calorimeter at R3B Open
The R3B experiment at FAIR studies nuclear reactions using high-energy radioactive beams. One key detector in R3B is the CALIFA calorimeter consisting of 2544 CsI(Tl) scintillator crystals designed to detect light charged particles and gam…
View article: Flow annealed importance sampling bootstrap meets differentiable particle physics
Flow annealed importance sampling bootstrap meets differentiable particle physics Open
High-energy physics requires the generation of large numbers of simulated data samples from complex but analytically tractable distributions called matrix elements. Surrogate models, such as normalizing flows, are gaining popularity for th…
View article: Is tokenization needed for masked particle modeling?
Is tokenization needed for masked particle modeling? Open
In this work, we significantly enhance masked particle modeling (MPM), a self-supervised learning scheme for constructing highly expressive representations of unordered sets relevant to developing foundation models for high-energy physics.…
View article: Reinterpretation and preservation of data and analyses in HEP
Reinterpretation and preservation of data and analyses in HEP Open
Data from particle physics experiments are unique and are often the result of a very large investment of resources. Given the potential scientific impact of these data, which goes far beyond the immediate priorities of the experimental col…
View article: The First Release of ATLAS Open Data for Research
The First Release of ATLAS Open Data for Research Open
The ATLAS Collaboration has released an extensive volume of Open Data for Research use for the first time. The full datasets of proton collisions from 2015 and 2016, alongside a wide array of matching simulated data, are all offered in the…
View article: Building a Columnar Analysis Demonstrator for ATLAS PHYSLITE Open Data using the Python Ecosystem
Building a Columnar Analysis Demonstrator for ATLAS PHYSLITE Open Data using the Python Ecosystem Open
The ATLAS experiment is in the process of developing a columnar analysis demonstrator, which takes advantage of the Python ecosystem of data science tools. This project is inspired by the analysis demonstrator from IRIS-HEP. The demonstrat…
View article: Optimization using pathwise algorithmic derivatives of electromagnetic shower simulations
Optimization using pathwise algorithmic derivatives of electromagnetic shower simulations Open
Among the well-known methods to approximate derivatives of expectancies computed by Monte-Carlo simulations, averages of pathwise derivatives are often the easiest one to apply. Computing them via algorithmic differentiation typically does…
View article: Lower dorsal raphe nucleus activity reflects resilience to Aß‐related cognitive decline in preclinical Alzheimer’s disease individuals with mild depressive symptoms
Lower dorsal raphe nucleus activity reflects resilience to Aß‐related cognitive decline in preclinical Alzheimer’s disease individuals with mild depressive symptoms Open
Background The neuromodulatory subcortical systems are among the earliest brain regions to accrue pathology in Alzheimer’s disease (AD), contributing to cognitive and non‐cognitive symptoms. Monoaminergic nuclei, such as the dorsal raphe (…
View article: Higher locus coeruleus integrity and cognitive reserve attenuate tau‐related cognitive decline in older adults
Higher locus coeruleus integrity and cognitive reserve attenuate tau‐related cognitive decline in older adults Open
Background Previous research suggests that locus coeruleus (LC) integrity may partially underlie cognitive reserve (CR), as demonstrated by positive associations between composite measures of CR and MRI‐based integrity of the LC, one of th…
View article: Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle Physics
Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle Physics Open
High-energy physics requires the generation of large numbers of simulated data samples from complex but analytically tractable distributions called matrix elements. Surrogate models, such as normalizing flows, are gaining popularity for th…
View article: HGPflow: Extending Hypergraph Particle Flow to Collider Event Reconstruction
HGPflow: Extending Hypergraph Particle Flow to Collider Event Reconstruction Open
In high energy physics, the ability to reconstruct particles based on their detector signatures is essential for downstream data analyses. A particle reconstruction algorithm based on learning hypergraphs (HGPflow) has previously been expl…
View article: Neural simulation-based inference of the neutron star equation of state directly from telescope spectra
Neural simulation-based inference of the neutron star equation of state directly from telescope spectra Open
Neutron stars provide a unique opportunity to study strongly interacting matter under extreme density conditions. The intricacies of matter inside neutron stars and their equation of state are not directly visible, but determine bulk prope…
View article: Dataset for flavour tagging R&D
Dataset for flavour tagging R&D Open
This is a dataset for flavour tagging R&D. It consists of b-jets, c-jets and light-jets in equal number and equal distributions of transverse momentum, pseudo-rapidity and track multiplicity. The jets are sampled from ttbar events produced…
View article: Masked particle modeling on sets: towards self-supervised high energy physics foundation models
Masked particle modeling on sets: towards self-supervised high energy physics foundation models Open
We propose masked particle modeling (MPM) as a self-supervised method for learning generic, transferable, and reusable representations on unordered sets of inputs for use in high energy physics (HEP) scientific data. This work provides a n…
View article: Constructing model-agnostic likelihoods, a method for the reinterpretation of particle physics results
Constructing model-agnostic likelihoods, a method for the reinterpretation of particle physics results Open
View article: How the Scientific Python ecosystem helps answer fundamental questions of the Universe
How the Scientific Python ecosystem helps answer fundamental questions of the Universe Open
The ATLAS experiment at CERN explores vast amounts of physics data to answer the most fundamental questions of the Universe. The prevalence of Python in scientific computing motivated ATLAS to adopt it for its data analysis workflows while…
View article: Finetuning foundation models for joint analysis optimization in High Energy Physics
Finetuning foundation models for joint analysis optimization in High Energy Physics Open
In this work we demonstrate that significant gains in performance and data efficiency can be achieved in High Energy Physics (HEP) by moving beyond the standard paradigm of sequential optimization or reconstruction and analysis components.…
View article: Optimization Using Pathwise Algorithmic Derivatives of Electromagnetic Shower Simulations
Optimization Using Pathwise Algorithmic Derivatives of Electromagnetic Shower Simulations Open
Among the well-known methods to approximate derivatives of expectancies computed by Monte-Carlo simulations, averages of pathwise derivatives are often the easiest one to apply. Computing them via algorithmic differentiation typically does…
View article: Analysis Facilities White Paper
Analysis Facilities White Paper Open
This white paper presents the current status of the R&D for Analysis Facilities (AFs) and attempts to summarize the views on the future direction of these facilities. These views have been collected through the High Energy Physics (HEP) So…
View article: Scalable ATLAS pMSSM computational workflows using containerised REANA reusable analysis platform
Scalable ATLAS pMSSM computational workflows using containerised REANA reusable analysis platform Open
In this paper we describe the development of a streamlined framework for large-scale ATLAS pMSSM reinterpretations of LHC Run-2 analyses using containerised computational workflows. The project is looking to assess the global coverage of B…
View article: Neural Simulation-Based Inference of the Neutron Star Equation of State directly from Telescope Spectra
Neural Simulation-Based Inference of the Neutron Star Equation of State directly from Telescope Spectra Open
Neutron stars provide a unique opportunity to study strongly interacting matter under extreme density conditions. The intricacies of matter inside neutron stars and their equation of state are not directly visible, but determine bulk prope…
View article: Combined track finding with GNN & CKF
Combined track finding with GNN & CKF Open
The application of Graph Neural Networks (GNN) in track reconstruction is a promising approach to cope with the challenges arising at the High-Luminosity upgrade of the Large Hadron Collider (HL-LHC). GNNs show good track-finding performan…